1.2 Contact details of resource persons and institutions involved in the assessment and documentation of the Technology

Name of project which facilitated the documentation/ evaluation of the Technology (if relevant)

Index Based Livestock Insurance, Kenya (IBLI)

Name of the institution(s) which facilitated the documentation/ evaluation of the Technology (if relevant)

International Livestock Research Institute (ILRI) - Kenya

1.3 Conditions regarding the use of data documented through WOCAT

When were the data compiled (in the field)?

24/01/2018

The compiler and key resource person(s) accept the conditions regarding the use of data documented through WOCAT:

Ja

1.4 Declaration on sustainability of the described Technology

Is the Technology described here problematic with regard to land degradation, so that it cannot be declared a sustainable land management technology?

Nee

2.1 Short description of the Technology

Definition of the Technology:

Index-Based Livestock Insurance (IBLI) is a product that was designed to help protect pastoralists and their livestock against the effects of prolonged forage scarcity. IBLI triggers payment to pastoralists when the forage situation deteriorates to levels considered to be severe, as compared to historical conditions over time.

2.2 Detailed description of the Technology

Description:

In the event of severe seasonal drought, forage and grazing resources are depleted and livestock may die because of starvation. Index Based Livestock Insurance (IBLI) is the technology applied in the provision of forage based livestock (asset) protection insurance. IBLI uses Normalized Difference Vegetation Index (NDVI), a satellite-derived indicator of the amount and vigor of vegetation, based on the observed level of photosynthetic activity (Tucker et al., 2005). NDVI is derived from 10-day composites of 250m filtered eMODIS processed by the United States’ National Aeronautics and Space Administration (NASA) and transformed by The Earth Resources Observation Systems (EROS) Data Center (EDC) of the US Geological Survey (USGS). NDVI data from the AQUA satellite platform are available from July 2002 to present at https://lta.cr.usgs.gov/emodis.

In the case of IBLI, the index is a deviation of cumulative forage availability (ZCumNDVI) in the insured season. It measures forage conditions over a defined time period and compares the observed NDVI over a particular season, with the observed NDVI over a given historical period (e.g. 15 years). A set threshold below which payouts must be made is called the trigger level. Therefore, when the forage situation in a given season is worse than the 20th worst season in the last 15 years, a payout is triggered. In other words, IBLI will compensate if the forage conditions fall below the worst 20th percentile of seasonal pasture levels cumulated over the historical drought seasons in the past 15 years. IBLI therefore uses the satellite-based vegetation index to measure forage availability and to trigger timely payouts to pastoralists based on the costs of providing supplementary feeds to the animals for the specific season that a drought is triggered.

For precision and proper administration each insured county is broken down into smaller units of insurance which are referred to as Unit Areas of Insurance (UAIs). Each UAI represents a geographical area whose forage availability index data are aggregated as one unit for insurance purposes. The identification of the geographic areas that constitute UAIs is a key step in IBLI contract design because the use of average NDVI over insurable units is based on the premise that drought is a covariate shock and that pastoral communities affected in a particular area are deemed to suffer in equal measure.Thus, for accuracy and acceptability, the index should be tightly correlated with forage scarcity and reflect relative conditions on the ground. Also forage access should be relatively homogeneous for herders residing in the same insurable unit. The process of demarcating UAIs is known as clustering. It is done through a combination of active participatory community engagement and scientific methods .

Insured pastoralists are not always paid when there is drought. They are only paid when drought is so severe that the amount of forage falls below the strike level. Insured pastoralists will not receive payments if the forage availability is more than the strike level. This is an annual policy which covers forage availability as measured by satellite Normalized Difference Vegetation Index (NDVI) in up to 2 rainy seasons as per the bimodal rain patterns in Kenya. Pastoralists who do not hold insurance contracts are not covered by under, and therefore do not receive payments, even if they live in the same UAIs as the ones who have insured their livestock.

2.3 Photos of the Technology

2.5 Country/ region/ locations where the Technology has been applied and which are covered by this assessment

2.6 Date of implementation

If precise year is not known, indicate approximate date:

• less than 10 years ago (recently)

2.7 Introduction of the Technology

Specify how the Technology was introduced:

• during experiments/ research

3.1 Main purpose(s) of the Technology

• reduce risk of disasters
• adapt to climate change/ extremes and its impacts
• create beneficial economic impact

3.2 Current land use type(s) where the Technology is applied

3.3 Further information about land use

Water supply for the land on which the Technology is applied:

• rainfed

Number of growing seasons per year:

• 2

3.4 SLM group to which the Technology belongs

• pastoralism and grazing land management

3.5 Spread of the Technology

Specify the spread of the Technology:

• evenly spread over an area

If the Technology is evenly spread over an area, indicate approximate area covered:

• 100-1,000 km2

3.6 SLM measures comprising the Technology

3.7 Main types of land degradation addressed by the Technology

3.8 Prevention, reduction, or restoration of land degradation

Specify the goal of the Technology with regard to land degradation:

• adapt to land degradation

4.1 Technical drawing of the Technology

4.2 Technical specifications/ explanations of technical drawing

Picture 1 &2: Monthly NDVI for each insured unit in a month is derived by averaging the 10-day NDVI values for each Insured Unit over the three 10-days periods in each month, where the 10-day NDVI value for each Insured Unit is derived by averaging the pixel-level NDVI of all the pixels that fall within the insured unit boundary.
Picture 3: An accumulation of monthly NDVI over the critical months of each season starting from the beginning of the season (March for LRLD and October for SRSD).
Picture 4: There is a representation of a cumulation of monthly NDVI (CumNDVI) which represents the evolution of green vegetation and can be used as a proxy of the green biomass that has developed, and as such a proxy of the available forage across the season.
Picture 5: Deviation of Cumulative NDVI from normal conditions of the particular season and insured unit (ZCumNDVI) - is derived by
subtracting CumNDVI derived for each season and each insured unit by the long-term historical average value and dividing by long-term historical standard deviation of the particular season (SRSD or LRLD) in each
insured unit. ZCumNDVI thus measures the deviation from the historical mean,
expressed as a standard deviation. A positive ZCumNDVI value therefore indicates above normal vegetation cover while negative ZNDVI value indicates below normal vegetation. The purpose of this transformation is to provide an indicator that expresses current forage conditions in comparison to average forage conditions for each specific insurance unit
at the prescribed period within the season.
NOTE: The delineations used in this figure are just for purposes of illustration, assuming Kenya as an entire unit. However, IBLI used smaller delineated units which can be seen in the lines with lighter shade within the map.

5.1 Climate

5.2 Topography

Indicate if the Technology is specifically applied in:

• not relevant

5.3 Soils

5.4 Water availability and quality

Is water salinity a problem?

Ja

Is flooding of the area occurring?

Nee

5.5 Biodiversity

5.6 Characteristics of land users applying the Technology

5.7 Average area of land owned or leased by land users applying the Technology

5.8 Land ownership, land use rights, and water use rights

Land ownership:

• communal/ village

Land use rights:

• communal (organized)

Water use rights:

• communal (organized)

5.9 Access to services and infrastructure

6.1 On-site impacts the Technology has shown

Socio-cultural impacts

Ecological impacts

Climate and disaster risk reduction

6.2 Off-site impacts the Technology has shown

6.3 Exposure and sensitivity of the Technology to gradual climate change and climate-related extremes/ disasters (as perceived by land users)

Gradual climate change

Gradual climate change

	Season	Type of climatic change/ extreme	How does the Technology cope with it?
annual rainfall		decrease	moderately

Climate-related extremes (disasters)

Climatological disasters

	How does the Technology cope with it?
drought	well

Other climate-related extremes (disasters)

other (specify)	How does the Technology cope with it?
Forage scarcity	well

6.4 Cost-benefit analysis

How do the benefits compare with the establishment costs (from land users’ perspective)?

How do the benefits compare with the maintenance/ recurrent costs (from land users' perspective)?

6.5 Adoption of the Technology

• 10-50%

If available, quantify (no. of households and/ or area covered):

over 25,000 households

Of all those who have adopted the Technology, how many have did so spontaneously, i.e. without receiving any material incentives/ payments?

• 10-50%

6.6 Adaptation

Has the Technology been modified recently to adapt to changing conditions?

Nee

6.8 Weaknesses/ disadvantages/ risks of the Technology and ways of overcoming them

Weaknesses/ disadvantages/ risks in the land user’s view	How can they be overcome?
A key concern of index-insurance products is that there might occur variances in perceived loss versus the actual payouts (basis risk)	Proper contract design should be backed with ground truthing efforts together with awareness creation.

7.1 Methods/ sources of information

7.2 References to available publications

Title, author, year, ISBN:

Chelang'a et. al 2017

Title, author, year, ISBN:

Tucker et al., 2005

7.3 Links to relevant information which is available online

Title/ description:

IBLI website

URL:

https://ibli.ilri.org/